Wednesday, March 25, 2015

Beauty pays. Why?



Are handsome people more productive?

In 1976, an attractive young secretary to a powerful US Congressman, Wayne Hays, acknowledged that she couldn’t type, file or answer the phone.  Elizabeth Ray's skills were rather more tactile than that.

In the past 20 years, economists have studied whether good looks lead to good wages.  The answer might help explain why fashion is a booming industry in Kazakhstan.  Production of clothes in January and February was 79% above that for the same period in 2014, according to the government’s committee on statistics.  The trick is to find an industry in which beauty adds nothing to productivity and yet earns a premium.  Congressmen may not be the only employers to discriminate in favor of knockouts.

The task is not simple.  Even in industries where beauty is not a work skill, it may enable the beautiful to produce more, because they are more self-assured.  In central banking, good looks don’t directly make inflation forecasts more accurate; but comely forecasters may have the confidence to present their tentative results to colleagues, gathering feedback that improves their work.  We must disentangle two effects of beauty on wages: The direct one, which reflects the employer’s tastes; and the indirect one, which reflects the employee’s confidence.  

In a 2005 experiment, Markus Mobius and Tanya Rosenblat construct a workplace in which beauty cannot directly affect productivity – the solving of computer mazes, reminiscent of those in white-rat studies.  In the experiment, each "worker" estimates the number of mazes that she thinks that she can complete in 15 minutes; this is a measure of her confidence.  A separate panel of high school students computes a beauty score for each worker by examining her photo.

Beauts and bias

Each "employer" estimates, for each worker, the number of mazes that she can solve, given what he knows of her, including her beauty.  These estimates are used to determine the worker’s expected wage.  Employers may learn about workers through resumes, photos, and interviews by phone or face-to-face.  If beauty matters to the employer, then he may estimate a higher productivity for a worker by examining her photo than he would have done had he only interviewed her by phone.

The researchers conclude that beauty raises the worker’s estimate of his own productivity – as well as the employers’ estimates.  Of the latter, roughly 80% relate to the bosses’ own observations of beauty, and 20% to the greater confidence of more beauteous workers. 

Beauty affects the employer’s estimate of productivity when he learns about the worker through photos, phone interviews, and -- above all -- through face-to-face interviews.  However, Mobius and Rosenblat find no evidence that employers discriminate in favor of the handsome.  They conclude this by looking at whether beauty increases the employer’s estimate of productivity when he knows that this calculation will determine the worker's wage.  Beauty has no such effect. 

In the study, good looks affect the employer’s estimate of productivity even when he only interviews her over the telephone.  Mobius and Rosenblat speculate that beauty may relate to “certain oral communication skills” aside from influencing confidence.  Maybe Ms. Ray should have learned how to answer the phone. 

Curiously, gender does not affect the worker’s confidence.  But men solve 30% more mazes than women do, perhaps because men are naturally rats.

The experiment involved 165 “employers” and 165 “employees” at La Universidad de Tucuman in Argentina.  –Leon Taylor tayloralmaty@gmail.com  


References

Markus M. Mobius and Tanya S. Rosenblat.  Why beauty matters.  American Economic Review 96(1): 222-35.  2006.  The working paper, upon which I draw, was published by the National Bureau of Economic Research in 2005.

Kazakhstan Ministry of National Economy, Committee on Statistics.  Socio-economic development of the Republic of KazakhstanFebruary 2015.  Online.

Wikipedia.  Elizabeth Ray.  Accessed March 25, 2015.

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